Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithm...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Information systems (Oxford) Jg. 53; S. 16 - 38
Hauptverfasser: Aghabozorgi, Saeed, Seyed Shirkhorshidi, Ali, Ying Wah, Teh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2015
Schlagworte:
Biology
Classification
Clustering
Distance measure
Evaluation measure
Finance
Gene expression
Information systems
Mathematical models
Raw materials
Representations
Time-series
Time-series
Representations
Clustering
Evaluation measure
Distance measure
ISSN:0306-4379, 1873-6076
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works. •Anatomy of time-series clustering is revealed by introducing its 4 main component.•Research works in each of the four main components are reviewed in detail and compared.•Analysis of research works published in the last decade.•Enlighten new paths for future works for time-series clustering and its components.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-4379
1873-6076
DOI:10.1016/j.is.2015.04.007